Peter Christiansen

Peter Hviid Christiansen, Mikkel Fly Kragh, Yury Brodskiy et al.

It is hard to create consistent ground truth data for interest points in natural images, since interest points are hard to define clearly and consistently for a human annotator. This makes interest point detectors non-trivial to build. In this work, we introduce an unsupervised deep learning-based interest point detector and descriptor. Using a self-supervised approach, we utilize a siamese network and a novel loss function that enables interest point scores and positions to be learned automatically. The resulting interest point detector and descriptor is UnsuperPoint. We use regression of point positions to 1) make UnsuperPoint end-to-end trainable and 2) to incorporate non-maximum suppression in the model. Unlike most trainable detectors, it requires no generation of pseudo ground truth points, no structure-from-motion-generated representations and the model is learned from only one round of training. Furthermore, we introduce a novel loss function to regularize network predictions to be uniformly distributed. UnsuperPoint runs in real-time with 323 frames per second (fps) at a resolution of $224\times320$ and 90 fps at $480\times640$. It is comparable or better than state-of-the-art performance when measured for speed, repeatability, localization, matching score and homography estimation on the HPatch dataset.

Timo Korthals, Mikkel Kragh, Peter Christiansen et al.

In recent years, the drive of the Industry 4.0 initiative has enriched industrial and scientific approaches to build self-driving cars or smart factories. Agricultural applications benefit from both advances, as they are in reality mobile driving factories which process the environment. Therefore, acurate perception of the surrounding is a crucial task as it involves the goods to be processed, in contrast to standard indoor production lines. Environmental processing requires accurate and robust quantification in order to correctly adjust processing parameters and detect hazardous risks during the processing. While today approaches still implement functional elements based on a single particular set of sensors, it may become apparent that a unified representation of the environment compiled from all available information sources would be more versatile, sufficient, and cost effective. The key to this approach is the means of developing a common information language from the data provided. In this paper, we introduce and discuss techniques to build so called inverse sensor models that create a common information language among different, but typically agricultural, information providers. These can be current live sensor data, farm management systems, or long term information generated from previous processing, drones, or satellites. In the context of Industry 4.0, this enables the interoperability of different agricultural systems and allows information transparency.

Mikkel Fly Kragh, Peter Christiansen, Morten Stigaard Laursen et al.

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.